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Published June 24, 2018 | Published
Conference Paper Open

A Bias-Aware EnKF Estimator for Aerodynamic Flows

Abstract

Ensemble methods can integrate measurement data and CFD-based models to estimate the state of fluid systems in a robust and cost-efficient way. However, discretization errors can render numerical solutions a biased representation of reality. Left unaccounted for, biased forecast and observation models can lead to poor estimator performance. In this work, we propose a low-rank representation for the bias whose dynamics is represented by a colorednoise process. System state and bias parameters are simultaneously corrected on-line with the Ensemble Kalman Filter (EnKF) algorithm. The proposed methodology is demonstrated to achieve a 70% error reduction for the problem of estimating the state of the two-dimensional low-Re flow past a flat plate at high angle of attack using an ensemble of coarse-mesh simulations and pressure measurements at the surface of the body, compared to a bias-blind estimator. Strategies to determine the bias statistics and to deal with nonlinear observation functions in the context of ensemble methods are discussed.

Additional Information

© 2018 by Andre Fernando de Castro da Silva. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Published Online: 24 Jun 2018. This work has been supported in part by a grant from AFOSR (FA9550-14-1-0328) with Dr. Douglas Smith as program manager. A.F.C. da Silva would like to thank the Ministry of Education of Brazil (Capes Foundation) for its support through a Science without Borders scholarship (Grant number BEX 12966/13-4). The authors also acknowledge Prof. David Williams (Illinois Institute of Technology), Prof. Jeff Eldredge (University of California, Los Angeles) and Prof. Andrew Stuart (California Institute of Technology) for helpful discussions of this work.

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Created:
August 19, 2023
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October 20, 2023